Symbolic planning is combined with learning in [1]}, [2]}, [3]}, [4]}. In  [1]}, the PLANQ-Learning algorithm uses a symbolic planner to shape the reward function based on the conditions defined which are then used by the Q-learner to get an optimal policy with good results on the grid domain. [2]} uses the combined symbolic planner with reinforcement learning (RL) in a hierarchical framework to solve complex visual interactive question answering tasks. PEORL [3]} integrates symbolic planning and hierarchical reinforcement learning (HRL) to improve performance by achieving rapid policy search and robust symbolic planning in the taxi domain and grid world. SPOTTER [4]} uses RL to allow the planning agent to discover the new operators required to complete tasks in Grid World. In contrast to all these approaches, our approach aims towards real-life robotic tasks in an Industry 4.0 setting where a digital twin is available.
